A new gradient free local search mechanism for constrained multi-objective optimization problems

Swarm and Evolutionary Computation - Tập 67 - Trang 100938 - 2021
Lourdes Uribe1, Adriana Lara1, Kalyanmoy Deb2, Oliver Schütze3
1ESFM – Instituto Politécnico Nacional, Mexico City, Mexico
2Michigan State University, East Lansing, MI, USA
3Department of Computer Science, Cinvestav-IPN, Mexico City, Mexico

Tài liệu tham khảo

Miettinen, 2012, volume 12 Peitz, 2018, A survey of recent trends in multiobjective optimal control–surrogate models, feedback control and objective reduction, Mathematical and Computational Applications, 23, 30, 10.3390/mca23020030 Deb, 2001 Coello Coello, 2007 Beume, 2007, SMS-EMOA: Multiobjective selection based on dominated hypervolume, Eur J Oper Res, 181, 1653, 10.1016/j.ejor.2006.08.008 Deb, 2014, An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints., IEEE Trans Evol Comp, 18, 577, 10.1109/TEVC.2013.2281535 Paul, 2015, Simultaneous feature selection and weighting–an evolutionary multi-objective optimization approach, Pattern Recognit Lett, 65, 51, 10.1016/j.patrec.2015.07.007 Liu, 2019, Handling constrained multiobjective optimization problems with constraints in both the decision and objective spaces, IEEE Trans. Evol. Comput., 23, 870, 10.1109/TEVC.2019.2894743 Fan, 2019, Push and pull search for solving constrained multi-objective optimization problems, Swarm Evol Comput, 44, 665, 10.1016/j.swevo.2018.08.017 Asafuddoula, 2012, An adaptive constraint handling approach embedded MOEA/D, 1 Jan, 2013, A study of two penalty-parameterless constraint handling techniques in the framework of MOEA/d, Appl Soft Comput, 13, 128, 10.1016/j.asoc.2012.07.027 Brown, 2005, Directed multi-objective optimization, International Journal of Computers, Systems, and Signals, 6, 3 Gao, 2008, Multi-objective optimization for the periodic operation of the naphtha pyrolysis process using a new parallel hybrid algorithm combining NSGA-II with SQP, Computers & Chemical Engineering, 32, 2801, 10.1016/j.compchemeng.2008.01.005 Lara, 2010, HCS: A new local search strategy for memetic multiobjective evolutionary algorithms, IEEE Trans Evol Comp, 14, 112, 10.1109/TEVC.2009.2024143 Takahama, 2010, Constrained optimization by the ε constrained differential evolution with an archive and gradient-based mutation, 1 Ghiasi, 2011, A non-dominated sorting hybrid algorithm for multi-objective optimization of engineering problems, Eng. Optim., 43, 39, 10.1080/03052151003739598 Zapotecas-Martínez, 2016, MONSS: A multi-objective nonlinear simplex search approach, Eng. Optim., 48, 16, 10.1080/0305215X.2014.992889 Datta, 2017, A radial boundary intersection aided interior point method for multi-objective optimization, Inf Sci (Ny), 377, 1, 10.1016/j.ins.2016.09.062 Sun, 2018 Fliege, 2000, Steepest descent methods for multicriteria optimization, Mathematical Methods of Operations Research, 51, 479, 10.1007/s001860000043 Schäffler, 2002, Stochastic method for the solution of unconstrained vector optimization problems, J Optim Theory Appl, 114, 209, 10.1023/A:1015472306888 Brown, 2003, Effective use of directional information in multi-objective evolutionary computation, 778 Bosman, 2011, On gradients and hybrid evolutionary algorithms for real-valued multiobjective optimization, IEEE Trans. Evol. Comput., 16, 51, 10.1109/TEVC.2010.2051445 Harada, 2006, Local search for multiobjective function optimization: Pareto descent method, 659 Fliege, 2009, Newton’S method for multiobjective optimization, SIAM J. Optim., 20, 602, 10.1137/08071692X Martín, 2018, Pareto tracer: a predictor–corrector method for multi-objective optimization problems, Eng. Optim., 50, 516, 10.1080/0305215X.2017.1327579 Schütze, 2016, The directed search method for multi-objective memetic algorithms, Comput Optim Appl, 63, 305, 10.1007/s10589-015-9774-0 Uribe, 2020, On the efficient computation and use of multi-objective descent directions within constrained moeas, Swarm Evol Comput, 52, 100617, 10.1016/j.swevo.2019.100617 Cheng, 2015, A new hybrid algorithm for multi-objective robust optimization with interval uncertainty, J. Mech. Des., 137, 10.1115/1.4029026 Neri, 2012, Memetic algorithms and memetic computing optimization: a literature review, Swarm Evol Comput, 2, 1, 10.1016/j.swevo.2011.11.003 Goh, 2008, volume 171 Shukla, 2007, On gradient based local search methods in unconstrained evolutionary multi-objective optimization, 96 Denysiuk, 2013, A new hybrid evolutionary multiobjective algorithm guided by descent directions, Journal of Mathematical Modelling and Algorithms in Operations Research, 12, 233, 10.1007/s10852-012-9208-2 Lara, 2013, The gradient free directed search method as local search within multi-objective evolutionary algorithms, 153 Nocedal, 1999 Schütze, 2017, Gradient subspace approximation: a direct search method for memetic computing, Soft comput, 21, 6331, 10.1007/s00500-016-2187-x Cuate, 2019, Variation rate to maintain diversity in decision space within multi-objective evolutionary algorithms, Mathematical and Computational Applications, 24, 82, 10.3390/mca24030082 Deb, 2002, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., 6, 182, 10.1109/4235.996017 Jain, 2014, An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part II: handling constraints and extending to an adaptive approach., IEEE Trans. Evolutionary Computation, 18, 602, 10.1109/TEVC.2013.2281534 Schütze, 2012, Using the averaged Hausdorff distance as a performance measure in evolutionary multiobjective optimization, IEEE Trans Evol Comp, 16, 504, 10.1109/TEVC.2011.2161872 Rudolph, 2016, Optimal averaged Hausdorff archives for bi-objective problems: theoretical and numerical results, Comput Optim Appl, 64, 589, 10.1007/s10589-015-9815-8 Bogoya, 2018, A (p, q)-averaged Hausdorff distance for arbitrary measurable sets, Mathematical and Computational Applications, 23, 51, 10.3390/mca23030051 Bogoya, 2019, The averaged Hausdorff distances in multi-objective optimization: a review, Mathematics, 7, 894, 10.3390/math7100894 Zitzler, 1998, Multi-Objective Optimization Using Evolutionary Algorithms a Comparative Case Study, 292 Zitzler, 2003, Performance assessment of multi-objective optimizers: an analysis and review, IEEE Trans. Evol. Comp., 7, 117, 10.1109/TEVC.2003.810758 Saha, 2012, Equality constrained multi-objective optimization, 1 Zhang, 2008, Multiobjective optimization test instances for the cec 2009 special session and competition, University of Essex, Colchester, UK and Nanyang technological University, Singapore, special session on performance assessment of multi-objective optimization algorithms, technical report, 264 Fan, 2020, Difficulty adjustable and scalable constrained multiobjective test problem toolkit, Evol Comput, 28, 339, 10.1162/evco_a_00259 Cuate, 2020, A benchmark for equality constrained multi-objective optimization, Swarm Evol Comput, 52, 100619, 10.1016/j.swevo.2019.100619 Li, 2018, Two-archive evolutionary algorithm for constrained multiobjective optimization, IEEE Trans. Evol. Comput., 23, 303, 10.1109/TEVC.2018.2855411 Deb, 2021, Surrogate Modeling Approaches for Multiobjective Optimization: Methods, Taxonomy, and Results, Math. Comput. Appl., 26 Beltran, 2020, The Pareto Tracer for General Inequality Constrained Multi-Objective Optimization Problems, Math. Comput. Appl., 25